Comparative Enzymatic Profiling of Enteric vs. Cortical Neural Stem Cells: Establishing a Methodological Foundation for Modeling Enteric Neurodegeneration

Comparative Enzymatic Profiling of Enteric vs. Cortical Neural Stem Cells: Establishing a Methodological Foundation for Modeling Enteric Neurodegeneration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Enzymatic Profiling of Enteric vs. Cortical Neural Stem Cells: Establishing a Methodological Foundation for Modeling Enteric Neurodegeneration Marzena Wojtaszewska, Oktawian Pawłowski, Jarosław Barski, Monika Sakowicz-Burkiewicz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9517347/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background The enteric nervous system (ENS) is increasingly implicated in the gut-brain axis pathophysiology of neurodegenerative diseases. Given the structural similarities between pathogenic amyloid-beta and industrial food protein amyloid fibrils (FPAFs), assessing enteric neurotoxicity is critical. However, current immortalized cell models fail to recapitulate specific neuro-glial vulnerabilities. Methods We established a robust primary model by isolating enteric neural stem cells (ENSCs) and cortical neural stem cells (NSCs) from E14.5 mouse embryos. Following comparable neuro-glial differentiation, we performed functional enzymatic profiling of glycolysis, the TCA cycle, and lipid metabolism. These profiles were mapped via multivariate analysis and compared alongside an Apoe knockout (KO) NSC lineage. Results ENSCs demonstrated a fundamentally distinct and more robust metabolic profile than cortical NSCs. ENSCs exhibited a potent mitochondrial-lipogenic axis—marked by significantly elevated citrate synthase, ATP-citrate lyase, and carnitine acetyltransferase—alongside a higher glycolytic flux (elevated lactate dehydrogenase). Principal Component Analysis confirmed ENSCs form a completely separate bioenergetic cluster. Furthermore, Apoe deficiency in cortical cells did not replicate this enteric phenotype but instead triggered a bioenergetic collapse and a stress-driven shift toward acetate utilization and peroxisomal oxidation. Conclusions The unique metabolic identity of the ENS disqualifies cortical or immortalized surrogates for accurate enteric modeling. This primary ENSC model, utilizing enzymatic activities as early biosensors, provides a crucial framework for evaluating the neurotoxic risks of dietary amyloidogenic proteins. enteric neural stem cells neural stem cells Apoe-knockout primary neurons enzymatic profile Figures Figure 1 Figure 2 1. Introduction The enteric nervous system (ENS) is frequently referred to as the "second brain" due to its extraordinary structural complexity and its ability to exert autonomous control over gastrointestinal functions [1]. Comprising approximately hundreds of millions of neurons in humans (with estimates often ranging between 400–600 million) and a dense, molecularly heterogeneous network of enteric glial cells (EGCs), the ENS exerts largely autonomous control over bowel function [2]. Recent advances in neurobiology have highlighted the critical role of the gut-brain axis [3], suggesting that alterations within the ENS may precede or contribute to the pathophysiology of central nervous system (CNS) disorders, including Parkinson’s disease (PD), Alzheimer’s disease (AD), and autism spectrum disorders (ASDs)[4]. However, the precise molecular mechanisms underlying these interactions remain elusive, largely due to the scarcity of physiologically relevant experimental models [5]. A growing body of evidence indicates that food processing technologies may lead to the formation of protein aggregates with fibrillar structures, remarkably similar to amyloid-beta (Aβ) fibrils [6, 7]. Given the well-documented neurotoxicity of soluble Aβ (1–42) oligomers in the CNS [8, 9], concerns have been raised regarding the potential impact of these industrial dietary fibrils on the enteric network [6, 7]. To date, most studies investigating intestinal cytotoxicity have relied on s immortalized intestinal epithelial cell lines [7, 10]. While useful for barrier studies, these models fail to capture the intricate neuro-glial interactions and the specific vulnerability of neuronal cells. Therefore, developing a robust primary model to assess the neurotoxicity of amyloidogenic proteins within the digestive tract is of paramount importance. Isolating and culturing primary enteric neurons and glia remains a significant technical challenge, characterized by low cellular yields and the risk of hypersensitivity induced by aggressive enzymatic digestion [11]. To circumvent these limitations, the cultivation of enteric neural stem cells (ENSCs) in the form of neurospheres offers a promising alternative [12]. Similar to their CNS counterparts—neural stem cells (NSCs) derived from the cerebral cortex—ENSCs retain high proliferative capacity, allowing for the generation of sufficient biomass for panel-based screening [12]. Furthermore, primary cultures better mimic in vivo physiology compared to immortalized lines and allow for the direct observation of neuro-glial units without the confounding influence of the mucosal lining or the systemic complexities of in vivo models [10–12]. In the present study, we propose a comparative approach by simultaneously isolating ENSCs and NSCs from the same E14 embryos. This unique model allows for a direct side-by-side analysis of the similarities and differences between the "two brains" under identical experimental conditions. We focus on evaluating the enzymatic activity profiles of key enzymes known to be sensitive to amyloid-beta exposure in the context of Alzheimer’s disease. By comparing these two populations, we aim to determine whether NSCs can serve as a surrogate model for enteric neurotoxicity and to evaluate the suitability of both models for future research on the toxicity of food-derived amyloidogenic proteins. 2. Materials and Methods 2.1. Materials and Reagents Unless otherwise specified, all chemical compounds and reagents were purchased from Sigma-Aldrich (St. Louis, MO, USA). Cell culture disposables were provided by Sarstedt (Nümbrecht, Germany). Spectrophotometric measurements were performed using an Ultraspec 3100 Pro spectrophotometer (Amersham Biosciences, Warsaw, Poland). 2.2. Animals and Ethical Approval All experimental procedures were conducted in accordance with the EU Directive 2010/63/EU and the International Council for Laboratory Animal Science (ICLAS) guidelines (Permission No. 50/2019). A total of 5 to 8 independent biological replicates (N=5−8) were used for each experimental group. Each biological replicate (N=1) represented a pooled batch of embryos obtained from a single pregnant female to ensure sufficient material and biological consistency. Mice were maintained under a 12-h light/dark cycle in an enriched environment with ad libitum access to food and water [13] . 2.3. Primary Neuronal Cultures (PR) Primary neurons were isolated from the cerebral cortices of C57/BL6 mouse embryos (E14) in Hank’s Balanced Salt Solution (HBSS) supplemented with 50 U/mL penicillin, 50 mg/mL streptomycin, and 8 mM HEPES (Thermo Fisher Scientific, Waltham, MA, USA). Following the removal of blood vessels, the tissue was mechanically dissociated. The cell suspension was quantified using a hemocytometer and plated at a density of 9×10 4 cells/mL in Neurobasal medium supplemented with B27, 2 mM L-glutamine, 100 U/mL penicillin, and 100 μg/mL streptomycin (Thermo Fisher Scientific). Culture surfaces were pre-coated with poly-L-ornithine (Sigma-Aldrich) and laminin (Thermo Fisher Scientific). After 24 h, the medium was replaced with fresh medium and cultured for additional 7 days at 37 ∘ C and 5%CO 2 [14]. Cell purity and lineage identity were maintained according to previously validated and published protocols [14]. 2.4. Neural Stem Cell Expansion and Differentiation (ENSC, NSC, Apoe KO-NSC) Enteric neuronal stem cells (ENSC) were isolated from the embryonic large intestine (E14.5), while cortical neural stem cells (NSC) were obtained from the cerebral cortices of wild-type C57/BL6 or transgenic ApoE knockout (B6.129P2−Apoetm1Unc/J) embryos (E14.5). Following dissection in HBSS (supplemented with HEPES and antibiotics), cells were expanded as free-floating neurospheres in serum-free DMEM/F12 with GlutaMAX (Gibco) containing B27 supplement, 10 ng/mL bFGF (Gibco), and 20 ng/mL EGF (Corning). During the expansion phase, neurospheres were maintained without routine quantification and passaged every 2–3 days. For experimental assays (passages 3–5), neurospheres were dissociated into a single-cell suspension, quantified, and seeded in a monolayer at a density of 3×10 4 cells/cm 2 on poly-L-ornithine/laminin-coated dishes. After 24 h, the medium was replaced with growth-factor-free medium to induce differentiation for 7 days at 37 ∘ C and 5%CO 2 [14]. Purity and lineage characteristics of the stem cell-derived cultures were consistent with established institutional standards and previous reports [14]. 2.5. Microscopic Imaging 2.5.1. Live-Cell Phase-Contrast Microscopy Morphology and viability of live cell cultures were monitored throughout the expansion and differentiation phases using an inverted light microscope (Axiovert 25, Zeiss, Oberkochen, Germany). Phase-contrast images were captured at 40x magnification to monitor cell development [14]. 2.5.2. Immunocytochemistry (ICC) Cells were fixed with 4% paraformaldehyde (PFA) for 15 min at RT, permeabilized, and blocked in 0.1% Triton X-100/PBS with 5% normal goat serum (NGS). Cells were incubated with primary antibodies (mouse anti-CNPase, 1:400, Sigma Aldrich; rabbit anti-β-III-tubulin, 1:500, Cell Signaling Technology; mouse anti-S100β, 1:500, Sigma Aldrich; rabbit anti-GFAP, 1:200, Abcam) overnight, followed by AlexaFluor-conjugated secondary antibodies (488/555 nm anti-rabbit, 488/555 nm anti-mouse anti-mouse; Thermo Fisher Scientific) for 30 min at 37 ∘ C. Nuclei were counterstained with DAPI stain (Thermo Fisher). Fluorescence images were acquired using a VS200 ASW inverted microscope (Olympus) at 60x magnification (2 image per each biological replicate) [13]. 2.6. Enzymatic Activity Assays and Protein Quantification Total protein content was assayed via the Bradford method using human immunoglobulin as the standard. All enzymatic activities were determined spectrophotometrically at 37 ∘ C (unless otherwise noted) and are reported as specific activity (Units per mg of protein, U/mg, or as defined in the Results section). Cells were lysed using 0.2% Triton X-100. 2.6.1. Tricarboxylic Acid (TCA) Cycle and Related Enzymes Aconitase (Aco, EC 4.2.1.3) and Isocitrate Dehydrogenase (IDH, EC 1.1.1.42): Activities were determined via NADPH/NADP conversion at λ=340nm. The Aco reaction buffer contained 0.05 M Tris-HCl (pH 7.4), 2 mM MgCl 2 , 0.1 mM NADP, and 1 U IDH-NADP, 10 µM cis- aconitane. The IDH buffer contained 0.05 M Tris-HCl (pH 7.4), 0.6 mM MgCl 2 , and 0.5 mM NADP, 10 µM isocitrate [14]. Citrate Synthase (CS, EC 4.1.3.7): Measured by DTNB reduction at λ=412nm in 0.1 M Tris-HCl (pH 8.0) with 0.015 mM acetyl-CoA and 0.2 mM DTNB, 0.2 mM oxaloacetate [14]. Lactate Dehydrogenase (LDH, EC 1.1.1.27): Assayed by NADH/NAD conversion at λ=340nm in 0.1 M Tris-HCl (pH 7.4) with 0.2 mM NADH and 0.1 M pyruvate [14]. Pyruvate Dehydrogenase Complex (PDHC, EC 1.2.4.1): Determined using a cycling method. Citrate production was carried out for 30 min at 37 ∘ C (0.1 M Tris-HCl pH = 8.3, 2 mM MgCl2, 10 mM DTT, 10 mM pyruvate, 2 mM thiamine pyrophosphate, 0.2 mM CoA, 2.5 mM oxaloacetate, 2 mM NAD, 0.15 U citrate synthase (EC 4.1.3.7).), followed by thermal termination (100 ∘ C). Citrate levels were then measured spectrophotometrically at λ=340nm using 0.2 U malate dehydrogenase and 0.1 U citrate lyase (0.1 M Tris-HCl pH = 7.4, 0.1 mM NADH) [14]. 2.6.2. Lipid Metabolism and Pentose Phosphate Pathway Enzymes Glucose-6-Phosphate Dehydrogenase (G6PD, EC 1.1.1.49): Assayed via NADPH/NADP conversion at λ=340nm in 0.05 M Tris-HCl (pH 7.4), 0.2 mM MgCl 2 , 25 mM NADP and 10 mM glucose-6-phosphate [15]. Fatty Acid Synthase (FAS, EC 2.3.1.85): Measured at λ=340nm in 0.05 M Tris-HCl (pH 7.4) with 0.1 M DTT, 0.1 M EDTA, 0.01 mM NADPH and 6 mg/ml malonyl-CoA [16]. Carnitine Acetyltransferase (CAT, EC 2.3.1.7): Determined via a cycling DTNB/TNB reduction technique at λ=412nm following acetyl-carnitine production (0.1 M Tris-HCl pH = 7.4, 0.15 M carnitine, 10 mg/ml acetyl-CoA) reaction termination with 40% trichloroacetic acid and CoA level assessment (0.05 M Tris-HCl pH = 7.4, 0.05 M EDTA, 2 mM DTNB) [17]. Acyl-CoA Oxidase 1 (ACOX1, EC 1.3.3.6): Activity was measured at λ=500nm and 30∘C using the phenol/quinoneimine conversion technique in 45 mM MES (pH 8.0) with 4-aminoantipyrine, FAD, phenol, peroxidase and 5 mg/ml palmityl-CoA [18]. Acetyl-CoA Synthetase (ACS, EC 6.2.1.1) and ATP-Citrate Lyase (ACLY, EC 2.3.3.8): Measured by NAD(P)H conversion at λ=340nm in 0.05 M Tris-HCl (pH = 7.4), 10 mM Acetate, 4 mM ATP, 1 mM MgCl 2 , 10 mM DTT and 6 mg/ml CoA [19, 20]. 2.7. Statistical Analysis Data distribution was confirmed as normal using the Kolmogorov–Smirnov test. Comparative analysis was performed using Student’s T-test (for two groups) or One-way ANOVA followed by Tukey’s multiple comparison post-hoc test. Multivariate data analysis, including Principal Component Analysis (PCA) and Permutational Multivariate Analysis of Variance (PERMANOVA), was performed to assess global metabolic differences between lineages using the vegan and factoextra packages. Data are presented as mean ± SD. All statistical procedures were conducted in RStudio (R version 4.5.2). 3. Results 3.1. Lineage Commitment and Model Validation Phase-contrast microscopy of live cultures at 7 DIV revealed no distinct morphological differences between differentiated ENSC, NSC, and Apoe KO lineages. All stem cell-derived groups formed comparable monolayers characterized by a similar cellular architecture and growth pattern. In contrast, primary cortical neurons (PR) exhibited a markedly lower overall cell density compared to the stem cell-derived lineages. Among the three stem cell-derived models, no significant variations in cell density or general morphology were observed under the identical experimental conditions used for differentiation (Fig. 1A). To further validate the cellular composition, we performed immunocytochemical staining for neuronal and glial markers (Fig. 1A-B). Quantitative analysis (Fig. 1B) confirmed that the differentiation protocol yielded similar lineage distributions across the ENSC, NSC, and Apoe KO groups. GFAP + glial cells constituted the predominant population, accounting for approximately 60–80% of total cells, while βIII-tubulin + neurons represented approximately 20–25% of the culture. A small, stable subpopulation of CNPase + cells (typically below 10%) was consistently identified in all groups (Fig. 1B). The high degree of morphological and compositional similarity between the stem cell-derived lineages ensures that the metabolic divergences identified in the subsequent enzymatic profiling reflect intrinsic biochemical programming rather than variations in cell type proportions or culture density. 3.2. Functional Enzymatic Profiling of Enteric and Cortical Lineages To establish the bioenergetic baseline for the proposed cell models, we quantified the activities of key enzymes involved in glycolysis, the tricarboxylic acid (TCA) cycle, and lipid metabolism (Table 1). Our results revealed fundamental differences in the metabolic "engines" of enteric versus cortical lineages, despite their shared embryonic origin (E14.5). The enteric neural stem cells (ENSCs) exhibited a significantly more robust metabolic profile compared to both cortical neural stem cells (NSCs) and primary cortical neurons (PR) (Table 1). Notably, the activity of lactate dehydrogenase (LDH), a key indicator of glycolytic flux and anaerobic capacity, was nearly 1.6-fold higher in ENSCs (1175.0±281.4 nmol/min/mg) than in NSCs (722.6±206.7 nmol/min/mg; p<0.05). Furthermore, citrate synthase (CS) activity, a widely accepted biomarker for mitochondrial content and TCA cycle entry, was over 3.5-fold higher in ENSCs compared to NSCs (212.9±85.8 vs. 58.9±21.5 nmol/min/mg), suggesting a substantially higher oxidative capacity in the enteric niche (Table 1). 3.3. Divergent Pathways in Lipid Metabolism and De Novo Lipogenesis A defining characteristic of the ENSC profile was the significantly elevated activity of enzymes supporting cytoplasmic lipid synthesis. ATP-citrate lyase (ACLY), which facilitates the transport of mitochondrial-derived citrate into the cytoplasm and its conversion back to acetyl-CoA for de novo lipogenesis, was significantly more active in ENSCs than in cortical cells (89.3±12.2 vs. 53.2±26.4 nmol/min/mg; p<0.05) (Table 1). This "pro-lipid" signature was further corroborated by the activity of isocitrate dehydrogenase (IDH). Given the use of NADP + as a cofactor in our assays, the measured activity primarily reflects the cytoplasmic/NADP-dependent isoforms, which play a critical role in supplying both citrate-derived intermediates and NADPH required for reductive biosynthesis. While primary cortical neurons (PR) displayed high IDH activity (81.7±24.9 nmol/min/mg), the basal enteric profile remained consistently robust compared to cortical progenitors. Additionally, carnitine acetyltransferase (CAT) activity was over four times higher in ENSCs than in NSCs (13.8±4.6 vs. 3.1±0.6 nmol/min/mg; p<0.05), confirming that ENSCs are uniquely optimized for intensive lipid-related metabolic turnover (Table 1). 3.4. Global Bioenergetic Mapping and Multivariate Analysis To assess the global bioenergetic relationships between the lineages, we performed a Principal Component Analysis (PCA) based on the activities of core energy-metabolism enzymes: LDH, PDHC, Aconitase, and IDH (Fig. 2A). The PCA model accounted for 75.4% of the total variance (PC1: 45.3%; PC2: 30.1%). The multivariate analysis revealed a striking metabolic overlap between primary cortical neurons (PR) and cortical neural stem cells (NSCs), with their 95% confidence ellipses largely coinciding. This similarity indicates a consistent metabolic program shared by cortical progenitors and their differentiated counterparts, characterized by a specific balance between pyruvate oxidation (PDHC) and TCA cycle flux (Fig. 2A). In contrast, ENSCs formed a distinct and separate cluster, clearly segregated from the cortical models along the PC2 axis. This global divergence was further validated using PERMANOVA, which confirmed that cell lineage is a significant determinant of the enzymatic profile (F=6.435,R2=0.518,p=0.0079). Pairwise PERMANOVA comparisons underscored that ENSCs differ significantly from both NSCs (p=0.0229) and PR (p=0.0237). Crucially, no significant metabolic difference was found between the two cortical models, NSC and PR (p=0.3537), suggesting that standard cortical surrogates fail to recapitulate the unique bioenergetic fingerprint required for accurate enteric modeling (Fig. 2A). 3.5. Comparative Metabolic Characterization of ENSC, NSC, and Apoe KO Lineages To determine whether the unique metabolic signature of the enteric niche could be recapitulated in cortical lineages by disrupting lipid transport, we compared ENSCs with wild-type NSCs (NSC) and Apoe knockout NSCs ( Apoe KO NSC). This comparison aimed to test if ApoE deficiency forces cortical cells toward a "pro-lipid" enteric phenotype. However, our results demonstrate that the loss of ApoE leads to a profound metabolic reorganization that does not mimic the enteric profile, but instead induces a distinct, potentially pathological bioenergetic state (Table 1). 3.5.1. Suppression of Glycolytic and Early TCA Cycle Flux in ApoE KO Cells A defining feature of the Apoe KO model was the drastic collapse of standard bioenergetic throughput. LDH activity in Apoe KO cells was over 10-fold lower than in NSCs (73.1±50.6 vs. 722.6±206.7 nmol/min/mg; p<0.001) and nearly 16-fold lower than in ENSCs (1175.0±281.4 nmol/min/mg). Furthermore, activities of enzymes initiating the TCA cycle and associated pathways were significantly suppressed; aconitase activity dropped to 10.6±2.2 nmol/min/mg, and IDH activity reached its lowest levels in this group (8.3±4.5 nmol/min/mg; p<0.05). Since the IDH assay was based on NADP + /NADPH turnover, these results specifically indicate a deficit in the cell’s capacity to generate NADPH via this pathway, which is critical for both reductive biosynthesis and antioxidant defense. 3.5.2. Shift Toward Alternative Carbon Utilization and Peroxisomal Oxidation In contrast to the physiologically balanced lipogenic profile of ENSCs, Apoe KO NSC cells exhibited a massive induction of acetyl-CoA synthetase (ACS) (210.5±149.4 vs. 17.7±4.8 nmol/min/mg in NSC; p<0.05), suggesting a forced reliance on acetate as a metabolic substrate. This was accompanied by a significant increase in acyl-CoA oxidase 1 (ACOX1) activity (48.6±10.6 nmol/min/mg; p<0.05), a hallmark of peroxisomal β-oxidation. Although glucose-6-phosphate dehydrogenase (G6PD) activity was elevated in Apoe KO NSC cells (88.8±33.4 nmol/min/mg), potentially as a compensatory mechanism to supply NADPH, the activity of fatty acid synthase (FAS) remained unchanged (45.2±30.0 nmol/min/mg). This indicates that the metabolic reprogramming in the absence of ApoE is driven by stress adaptation rather than the specialized lipogenic specialization seen in ENSCs. 3.6. Multivariate Bioenergetic Mapping (PCA Panel B) The global divergence of these models was further confirmed by multivariate mapping (Fig. 2B). The PCA model, encompassing 9 enzymatic variables, explained 68.1% of the total variance. Apoe KO NSC cells (purple ellipse) formed a highly distinct cluster, separated from both NSCs and ENSCs primarily along the PC1 axis (45.5% of variance). This separation was strongly driven by the loading vectors of ACS, ACOX1, and G6PD, confirming the shift toward an acetate-peroxisomal axis. Global PERMANOVA analysis validated that the metabolic fingerprint was significantly defined by the cell lineage and genotype (p=0.0079), with pairwise comparisons confirming that the Apoe KO profile remained significantly distinct from the ENSC signature (p<0.05). These data underscore that ApoE deficiency fails to recapitulate the enteric metabolic imprinting. Table 1. Comparison of enzymatic profiles across primary cell lines Cell line: ENSC NSC Apoe KO PR Enzyme activity nmol/min/mg protein Mean SD Mean SD Mean SD Mean SD LDH 1175.0 281.4 722.6* 206.7 73.1*** 50.6 819.7* 98.4 PDHC 12.2 2.8 19.0 4.8 21.0* 5.7 Aconitase 31.2 9.0 39.5 16.2 10.6* 2.2 36.4 9.7 IDHC 44.3 14.5 44.2 23.3 8.3* 4.5 81.7* 24.9 SC 212.9 85.8 58.9 21.5 264.9 252.8 ACOX1 26.5 8.0 24.6 8.4 48.6* 10.6 ACS 20.3 6.9 17.7 4.8 210.5* 149.4 G6PD 28.0 6.7 54.7 20.1 88.8* 33.4 ACLY 89.3 12.2 53.2* 26.4 83.1 15.9 FAS 42.1 20.8 33.3 9.7 45.2 30.0 CAT 13.8 4.6 3.1* 0.6 Data are presented as mean ± SD from N=5–8 independent biological replicates (each replicate represents a pooled batch of embryos from a single pregnant female). Statistical significance was determined by one-way ANOVA followed by Tukey’s multiple comparisons post-hoc test. Significance levels are indicated as follows: *p<0.05, **p<0.01, ***p<0.001 versus the ENSC group. Abbreviations: ENSC enteric neural stem cells, NSC cortical neural stem cells, Apoe KO apolipoprotein E knockout cortical neural stem cells, PR primary cortical neurons, LDH lactate dehydrogenase, PDHC pyruvate dehydrogenase complex, IDHC isocitrate dehydrogenase, SC citrate synthase, ACOX1 acyl-CoA oxidase 1, ACS acetyl-CoA synthetase, G6PD glucose-6-phosphate dehydrogenase, ACLY ATP-citrate lyase, FAS fatty acid synthase, CAT carnitine acetyltransferase. 4. Discussion 4.1. The Dual Nature of Protein Fibrillation: Industrial Utility vs. Pathogenic Risk The phenomenon of protein fibrillation, once primarily the domain of clinical pathology, has emerged as a significant factor in modern food science and technology. Thermal processing, such as the boiling of hen eggs, induces the fibrillation of key proteins like ovalbumin (OVA) and lysozyme (LYZ) [21, 22]. Traditionally, this structural transformation has been viewed through a positive lens by gastroenterologists and nutritionists, as it can attenuate the allergenic potential of eggs and enhance postprandial satiety. From a textural engineering perspective, these fibrils have revolutionized the development of vegetarian meat substitutes, where they serve as scaffolds that faithfully replicate the fibrous architecture of animal muscle tissue [6, 23]. However, the structural characteristics that make Food Protein Amyloid Fibrils (FPAFs) industrially attractive—their highly ordered β-sheet architectures, remarkable mechanical rigidity, and resistance to environmental degradation—are the same traits that define pathogenic amyloid proteins [23]. The pathomechanism of amyloidosis, particularly Alzheimer’s disease (AD), is fundamentally rooted in the neurotoxicity of soluble amyloid-beta (1-42) polymers, which share a cross-β structural motif with FPAFs [23]. This structural homology raises critical questions regarding the safety of a global food chain increasingly enriched with amyloid-like structures . Due to the extensive hydrogen bonding and the "dry" packing interface between their side chains, these fibrils are arguably the most stable structures known to be adopted by polypeptides [23, 24]. They possess melting temperatures and detergent resistance far exceeding those of globular proteins. While food processing often involves extreme conditions (e.g., low pH or prolonged heating), the question remains whether these fibrils, once formed, can withstand the varied chemical and physical rigors of the human digestive tract, especially in processing steps involving intense shear forces, such as the formation of foams and emulsions [23] [7]. 4.2. Amyloid Biohazards in the Human Food Chain Research has identified AA amyloid (amyloid A) deposits in the tissues of livestock deemed healthy for slaughter. Studies indicate a prevalence of 1% to 15% in cattle, with the variance largely driven by animal age—Italian cattle, being significantly older than their Swedish counterparts, showed the highest incidence [25]. Similar findings have been reported in sheep and directly in gourmet products such as foie gras [26, 27]. Under laboratory conditions, common food proteins like lysozyme and β-lactoglobulin have been shown to form amyloid-like aggregates. While some argue that these food-derived fibrils do not directly accelerate Aβ aggregation in the brain, the broader spectrum of systemic amyloidosis cannot be ignored [28]. Amyloidosis is a heterogeneous group of diseases involving the deposition of toxic, insoluble protein aggregates in multiple organs, leading to restrictive cardiomyopathy, renal failure, and severe soft-tissue involvement [29]. Cardiac involvement is particularly lethal, with a 5-year survival rate of less than 10% in advanced cases. Renal dysfunction, characterized by asymptomatic albuminuria progressing to nephrotic syndrome, is a hallmark of hereditary and systemic forms involving lysozyme (ALys) and AA amyloid [30, 31]. The most pervasive of these conditions remains Alzheimer’s disease. AD is characterized by the accumulation of soluble Aβ(1-42) oligomers in the brain, leading to the degradation of cholinergic neurons, synaptic dysfunction, and chronic neuroinflammation [10, 32]. Emerging evidence suggests that processed, protein-rich foods could serve as a source of protease-resistant amyloid aggregates that compromise nutritional value and potentially exaggerate the etiology of amyloid-related diseases [33, 34]. 4.3. Evaluating the Sensitivity of Current Experimental Models Despite statistical evidence linking neurodegenerative sensitivity to amyloid-like proteins, the current literature predominantly relies on the Caco-2 (colorectal adenocarcinoma) or HepG2 (immortalized liver) cell lines to assess the toxicity of food fibrils [7, 35–38]. Both Caco-2 and HepG2 are secondary cancer-derived lines, which are inherently more resistant to cytotoxic insults than neural tissue [39]. Studies have shown that while cancer lines might survive certain exposures, primary neurons are significantly more sensitive to apoptosis and necrosis . For example, the toxic response of neurons to quercetin is far more pronounced than that of neuroblastoma cell lines [40]. Furthermore, the mechanism of Aβ neurotoxicity is closely tied to neuron-specific proteins. The presence of Tau protein and cyclin-dependent kinase 5 (CDK5) in neurons is a critical trigger for polymer-induced neurotoxicity—proteins that are absent or significantly different in colorectal epithelial cells [41–43]. Recent evidence demonstrates that while Caco-2 cells may remain unchanged by animal-derived amyloid fibrils, these same fibrils cause significant upregulation of neurodegenerative disease proteins in neural models [7]. Experiments in C. elegans have further confirmed that certain amyloid species can cross the intestinal membrane and diffuse into surrounding tissues [44]. This disparity necessitates the use of more sensitive models, particularly those reflecting the enteric nervous system (ENS). The ENS is an essential link in gastrointestinal pathologies like necrotizing enterocolitis, diabetic gastroparesis, and Hirschsprung disease. 4.4. The Impact of Digestion and Pathophysiological Conditions A prevailing defense for the safety of food fibrils is their presumed degradation by gastrointestinal proteases like pepsin, trypsin, and chymotrypsin. Indeed, under optimal conditions, ThT staining and atomic force microscopy indicate that these enzymes can reduce fibril levels below detection limits [7, 45, 46]. However, recent reports suggesting complete degradation to non-toxic peptides are often based on models that simulate ideal physiological conditions. These models fail to account for the rising prevalence of gastrointestinal dysfunctions [7]. Conditions such as hypochlorhydria (frequently induced by proton pump inhibitors), pernicious anemia, or chloride imbalances directly impair pepsin activity and alter gastric pH. In such pathophysiological states, the digestion of food fibrils may be arrested at the stage of soluble oligomers and protofibrils [47]. These intermediate forms are far more dangerous than mature, insoluble fibrils; they are mobile, highly toxic, and capable of mucosal penetration. In vitro evidence even suggests that partial pepsin digestion can increase the toxicity of certain materials for neurons by releasing these dangerous soluble species [10]. Our findings indicate that ENSCs (enteric neural stem cells) possess a unique enzymatic profile, including 4-fold higher catalase (CAT) activity and high ACLY and SC activities, compared to cortical NSCs (Table 1). This may represent an evolutionary adaptation allowing these cells to neutralize proteotoxic stress that would be fatal to CNS neurons. The lack of neural-based models in existing food fibril literature makes our findings vital for understanding the true risks within the gut-brain axis. 4.5. Establishing the Primary ENSC and NSC Models The technical difficulty of isolating primary ENSCs is a major hurdle in neurobiological research. Cellular yields from adult and newborn mice are typically low, and their proliferative capacity in vitro is often poor [48]. While some teams have found that reducing oxygen to 5% improves isolation efficiency in adult swine models [49], we focused on E14.5 mouse embryos to maximize isolation efficiency and biomass (Fig. 1 A-B). Our protocol yielded cultures with a complex, network-like morphology containing βIII-tubulin (+) neurons, GFAP (+) / S100B (+) glial cells, and CNPase (+) myelinating cells (Fig. 1 A-B). This composition allows for the study of the neuro-glial unit, which is critical since the neurotoxicity of amyloid proteins often involves the disruption of energy production pathways, including the tricarboxylic acid (TCA) cycle [50, 51]. 4.6. Model Validation: NSC as a Tool for CNS Neuro-Glial Interplay To address these limitations, we compared primary cortical neurons (PR) with cortical neural stem cells (NSC). Our findings revealed a striking overlap in the metabolic profiles of these two groups, as visualized in the PCA analysis where their 95% confidence ellipses largely coincided (Fig. 2A). Importantly, this does not merely suggest that NSCs are a substitute for neurons. Instead, it validates the differentiated NSC model as a robust and essential tool for evaluating neuro-glial interactions within the CNS [52]. Unlike isolated primary neurons, which often lack a supporting glial environment, our differentiated NSC cultures yielded a balanced population of βIII-tubulin+ neurons (20-25%) and GFAP+/S100β(+) glial cells (60-80%)(Fig. 1 A-B)[14]. This composition is crucial for modeling the metabolic and inflammatory responses of the brain, as glial cells are active participants in both neuroprotection and the progression of amyloid-induced damage [13]. The metabolic consistency between PR and differentiated NSCs confirms that the latter can faithfully replicate the enzymatic and bioenergetic environment of the cortical niche while providing the biomass necessary for detailed molecular studies (Table 1). 4.7. The Enteric "Second Brain": ENSC vs. NSC Metabolic Divergence The most significant finding of this study is the definitive metabolic segregation of enteric neural stem cells (ENSCs) from their cortical counterparts. Despite sharing the same embryonic origin (E14.5), ENSCs form a completely distinct cluster in multivariate mapping. This global divergence (confirmed by PERMANOVA, p=0.0079) disqualifies standard cortical models from being used to accurately predict toxicity within the enteric nervous system (ENS)(Fig. 2 A). ENSCs exhibited a significantly more "aggressive" metabolic profile. The nearly 1.6-fold higher activity of Lactate Dehydrogenase (LDH) (1175.0 vs 722.6 nmol/min/mg) in ENSCs points to a strong preference for aerobic glycolysis (the Warburg effect) (Table 1). This is a hallmark of highly regenerative stem cells situated in niches—like the intestinal wall—where oxygen levels and chemical environments fluctuate constantly. [53] Simultaneously, ENSCs maintained a massive mitochondrial reserve, with citrate synthase (SC) activity being over 3.5 times higher than in NSCs (212.9 vs 58.9) (Table 1). The ENS must function autonomously in a volatile environment (fluctuating pH, microbial metabolites); this high mitochondrial capacity likely provides the energy necessary for such high-intensity metabolic turnover [54]. 4.7.1. The Anabolic Synergy of the "Second Brain" The defining characteristic of the ENSC is its "aggressive" anabolic signature, driven by the synergy of citrate synthase (SC) produces citrate in the mitochondria (212.9 in ENSC vs 58.9 in NSC), ATP-citrate lyase (ACLY) cleaves cytosolic citrate back into acetyl-CoA (89.3 vs 53.2); and carnitine ccetyltransferase (CAT) buffers the acetyl-CoA pool through the carnitine system (13.8 vs 3.1)(Table 1). This system is uniquely optimized for de novo lipogenesis and rapid membrane reconstruction [55]. While cortical NSCs direct glucose primarily toward the pentose phosphate pathway (higher G6PD: 54.7 vs 28.0) for ribose and NADPH production, ENSCs "pump" their carbon directly into cell mass (lipids) and rapid energy (LDH) (Table 1). The 4-fold increase in CAT suggests a superior ability to move two-carbon units where needed, providing metabolic flexibility that cortical neurons lack. This flexibility may also regulate histone acetylation, potentially explaining the greater phenotypic plasticity of enteric cells [56]. 4.7.2. Alzheimer’s as "Type 3 Diabetes" and the Braak Hypothesis In neurochemistry, AD is often referred to as "Type 3 Diabetes" due to systemic insulin resistance and metabolic dysfunction [57]. Our model highlights significant differences in how the "two brains" handle metabolic stress (Table 1, Fig. 2 A-B). The high glycolytic and mitochondrial turnover of the ENS might make it more sensitive to toxins that disrupt lipid metabolism—precisely the effect seen with some food-derived amyloids (Table 1). Furthermore, our results align with the Braak Hypothesis, which suggests that neurodegenerative pathologies (like Parkinson’s or AD) may originate in the enteric plexuses before "traveling" to the CNS via the vagus nerve [58]. The high metabolic rate of ENSCs could paradoxically make them more vulnerable; high mitochondrial turnover generates more reactive oxygen species (ROS), which can act as a primer for protein aggregation upon exposure to dietary fibrils [59]. Using the activities of aconitase (Aco) and isocitrate dehydrogenase (IDH) as "biosensors" allows us to detect this "cellular poisoning" before morphological degeneration occurs [60, 61]. Aconitase, with its Fe-S clusters, is particularly sensitive to the ROS generated by Aβ oligomers [62]. 4.8. The Apoe KO Model: Metabolic Collapse vs. Enteric Adaptation To test if disrupting lipid transport could "force" a cortical cell into an enteric-like state, we compared ENSCs with Apoe knockout ( Apoe KO) NSCs. ApoE is a critical risk factor for AD and is expressed in the gut as well as the brain [63]. Contrary to our initial hypothesis, ApoE deficiency did not mimic the enteric profile. Instead, it triggered a bioenergetic collapse, highlighted by a 10-fold drop in LDH activity (73.1 vs 722.6 in NSC) (Table 1). Apoe KO cells entered a "rescue metabolism" state, marked by a massive induction of Acetyl-CoA Synthetase (ACS) (210.5 vs 17.7) and acyl-CoA oxidase 1 (ACOX1) (48.6 vs 24.6) (Table 1). ACS allows cells to utilize acetate as a substrate [64]. Since acetate is a major short-chain fatty acid (SCFA) produced by the gut microbiome, our data suggest that in the absence of ApoE, neural cells become dangerously dependent on microbial metabolites. The induction of ACOX1 indicates increased peroxisomal fatty acid oxidation, which generates high levels of oxidative stress [65, 66]. This suggests that individuals with risk genotypes (like ApoE4) possess a lower metabolic threshold in their enteric nervous system [67]. Their "second brain" is already running on a "rescue" program, leaving no reserve to fight the proteotoxic stress of food-derived amyloid fibrils. This could explain why certain populations are more susceptible to the gut-initiated spread of neurodegeneration. 5. Conclusion The present study demonstrates that ENSCs possess a unique and robust metabolic identity that is fundamentally distinct from cortical NSCs. This identity is defined by a high-capacity mitochondrial-lipogenic axis (SC-ACLY-CAT) and a glycolytic preference (LDH) that likely protects them in the volatile gastrointestinal environment but also increases their vulnerability to dietary metabolic toxins. Our findings disqualify cortical surrogates and secondary lines like Caco-2 for accurate enteric modeling. By establishing enzymatic activities as early "biosensors," we provide a new framework for evaluating the long-term neurotoxic potential of food protein amyloid fibrils and their contribution to the global burden of neurodegenerative disease. Declarations Author Contribution Information M.W. (investigation), O.P. (formal analysis), A.G. (methodology, writing—review and editing), J.B. (methodology, writing—review and editing), M.S.B. (investigation, methodology, writing—review and editing), M.Z. (conceptualization, methodology, resources, data curation, writing—original draft preparation, writing—review and editing, visualization, supervision, project administration, funding acquisition). All authors have read and agreed to the published version of the manuscript. Funding This work was supported by the Medical University of Gdańsk through the "Excellence Initiative – Research University" (IDUB) framework, specifically within the "Young Creator of Science" grant program (grant no. 71-01409/ K01 0003279 and 71-01419/ K07 0004722). Competing Interest information The authors have no relevant financial or non-financial interests to disclose Data Availability The datasets generated during the current study are available from the corresponding author on request. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9517347","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634588136,"identity":"57f17c7e-4856-4213-afd9-9800be2f9fcb","order_by":0,"name":"Marzena Wojtaszewska","email":"","orcid":"","institution":"Medical University of Gdansk","correspondingAuthor":false,"prefix":"","firstName":"Marzena","middleName":"","lastName":"Wojtaszewska","suffix":""},{"id":634588137,"identity":"fb52628d-b9c6-4c11-97fe-5e82795eb84e","order_by":1,"name":"Oktawian Pawłowski","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Oktawian","middleName":"","lastName":"Pawłowski","suffix":""},{"id":634588140,"identity":"83114104-a2d2-40ee-bc04-ca9d7032a62b","order_by":2,"name":"Jarosław Barski","email":"","orcid":"","institution":"Medical University of Silesia","correspondingAuthor":false,"prefix":"","firstName":"Jarosław","middleName":"","lastName":"Barski","suffix":""},{"id":634588143,"identity":"35bb18bd-ff7d-44cf-a175-0aba21225480","order_by":3,"name":"Monika Sakowicz-Burkiewicz","email":"","orcid":"","institution":"Medical University of Gdansk","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"","lastName":"Sakowicz-Burkiewicz","suffix":""},{"id":634588144,"identity":"e29b69a2-fa43-41dd-ac1e-6fbc0d339310","order_by":4,"name":"Marlena Zyśk","email":"data:image/png;base64,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","orcid":"","institution":"Medical University of Gdansk","correspondingAuthor":true,"prefix":"","firstName":"Marlena","middleName":"","lastName":"Zyśk","suffix":""}],"badges":[],"createdAt":"2026-04-24 12:40:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9517347/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9517347/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108641998,"identity":"85100013-9fb5-4989-b0d1-7beabeb6327d","added_by":"auto","created_at":"2026-05-06 20:09:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":662820,"visible":true,"origin":"","legend":"\u003cp\u003eMorphological characterization and lineage commitment of enteric and cortical neural stem cell-derived cultures. (A) Representative phase-contrast and immunofluorescence images of enteric neural stem cells (ENSC), cortical neural stem cells (NSC), \u003cem\u003eApoe\u003c/em\u003e -knockout NSCs (\u003cem\u003eApoe\u003c/em\u003eKO), and primary cortical neurons (PR) at 7 days in vitro (DIV). Lineage commitment was confirmed using βIII-tubulin (red; neurons), CNPase (green; myelinating lineage), S100β (red; glia), and GFAP (green; glia). Nuclei were counterstained with DAPI (blue). (B) Quantitative analysis of lineage distribution expressed as a percentage of total DAPI+ nuclei. Graphs represent the proportion of GFAP+, βIII-tubulin+,and CNPase+ cells across experimental groups. Data are presented as mean ± SD from N=5−8 independent biological replicates. Statistical analysis (one-way ANOVA followed by Tukey's post-hoc test) showed no significant differences in lineage distribution between groups (p\u0026gt;0.05).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9517347/v1/708acdf940051516ab052f7e.png"},{"id":108642000,"identity":"0796bdc0-c159-4cd3-b4f3-08402361c9f2","added_by":"auto","created_at":"2026-05-06 20:09:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230659,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) of enzymatic activity profiles across cell lineages. (A) PCA biplot comparing enteric neural stem cells (ENSC, orange), cortical neural stem cells (NSC, blue), and primary cortical neurons (PR, green). Global separation between groups was statistically significant (PERMANOVA, F=6.435, R2=0.518, p=0.0079). Pairwise comparisons revealed significant differences between ENSC vs. NSC (p=0.0229) and ENSC vs. PR (p=0.0237), while no significant difference was observed between NSC and PR (p=0.3537). (B) PCA biplot comparing ENSC (orange), NSC (blue), and \u003cem\u003eApoe \u003c/em\u003eKO (purple) based on a metabolic panel of 9 variables. Pairwise PERMANOVA confirmed significant metabolic divergence between all groups (p\u0026lt;0.05). Specifically, ENSC remained significantly distinct from NSC, while \u003cem\u003eApoe \u003c/em\u003eKO exhibited a unique metabolic trajectory, failing to shift toward the enteric-like profile despite the disruption of lipid transport pathways. PC1 and PC2 explain 68.1% of the total variance, with \u003cem\u003eApoe \u003c/em\u003eKO separation driven primarily by elevated activities of Acyl-CoA oxidase and acetylCoA synthetase. Individual points represent independent biological replicates (N=5 per group). Shaded areas represent 95% confidence ellipses. Black arrows indicate the loading vectors of the variables, showing their contribution to the principal components.\u003c/p\u003e\n\u003cp\u003eAbbreviations: \u003cem\u003eLDH\u003c/em\u003e lactate dehydrogenase, \u003cem\u003ePDHC\u003c/em\u003epyruvate dehydrogenase complex, \u003cem\u003eAco\u003c/em\u003e aconitase, \u003cem\u003eIDHC\u003c/em\u003e isocitrate dehydrogenase, \u003cem\u003eG6PD\u003c/em\u003e glucose-6-phosphate dehydrogenase, \u003cem\u003eAcyl-CoA oxidase\u003c/em\u003e acyl-CoA oxidase 1, \u003cem\u003eacetylCoA synthetase\u003c/em\u003e acetyl-CoA synthetase, \u003cem\u003eFAS\u003c/em\u003e fatty acid synthase, \u003cem\u003ecitrate synthase\u003c/em\u003e citrate synthase, \u003cem\u003eATP citrate lyase\u003c/em\u003e ATP-citrate lyase.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9517347/v1/94cb88c9c2cea10006d564d0.png"},{"id":108806441,"identity":"a7d61138-1639-4678-a482-6cc62caca800","added_by":"auto","created_at":"2026-05-08 15:28:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1102734,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9517347/v1/be6f9f3d-1ec4-4cd2-8f1e-173843d99c9e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Enzymatic Profiling of Enteric vs. Cortical Neural Stem Cells: Establishing a Methodological Foundation for Modeling Enteric Neurodegeneration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe enteric nervous system (ENS) is frequently referred to as the \"second brain\" due to its extraordinary structural complexity and its ability to exert autonomous control over gastrointestinal functions [1]. Comprising approximately hundreds of millions of neurons in humans (with estimates often ranging between 400\u0026ndash;600\u0026nbsp;million) and a dense, molecularly heterogeneous network of enteric glial cells (EGCs), the ENS exerts largely autonomous control over bowel function [2]. Recent advances in neurobiology have highlighted the critical role of the gut-brain axis [3], suggesting that alterations within the ENS may precede or contribute to the pathophysiology of central nervous system (CNS) disorders, including Parkinson\u0026rsquo;s disease (PD), Alzheimer\u0026rsquo;s disease (AD), and autism spectrum disorders (ASDs)[4]. However, the precise molecular mechanisms underlying these interactions remain elusive, largely due to the scarcity of physiologically relevant experimental models [5].\u003c/p\u003e \u003cp\u003eA growing body of evidence indicates that food processing technologies may lead to the formation of protein aggregates with fibrillar structures, remarkably similar to amyloid-beta (Aβ) fibrils [6, 7]. Given the well-documented neurotoxicity of soluble Aβ (1\u0026ndash;42) oligomers in the CNS [8, 9], concerns have been raised regarding the potential impact of these industrial dietary fibrils on the enteric network [6, 7]. To date, most studies investigating intestinal cytotoxicity have relied on s immortalized intestinal epithelial cell lines [7, 10]. While useful for barrier studies, these models fail to capture the intricate neuro-glial interactions and the specific vulnerability of neuronal cells. Therefore, developing a robust primary model to assess the neurotoxicity of amyloidogenic proteins within the digestive tract is of paramount importance.\u003c/p\u003e \u003cp\u003eIsolating and culturing primary enteric neurons and glia remains a significant technical challenge, characterized by low cellular yields and the risk of hypersensitivity induced by aggressive enzymatic digestion [11]. To circumvent these limitations, the cultivation of enteric neural stem cells (ENSCs) in the form of neurospheres offers a promising alternative [12]. Similar to their CNS counterparts\u0026mdash;neural stem cells (NSCs) derived from the cerebral cortex\u0026mdash;ENSCs retain high proliferative capacity, allowing for the generation of sufficient biomass for panel-based screening [12]. Furthermore, primary cultures better mimic in vivo physiology compared to immortalized lines and allow for the direct observation of neuro-glial units without the confounding influence of the mucosal lining or the systemic complexities of in vivo models [10\u0026ndash;12].\u003c/p\u003e \u003cp\u003eIn the present study, we propose a comparative approach by simultaneously isolating ENSCs and NSCs from the same E14 embryos. This unique model allows for a direct side-by-side analysis of the similarities and differences between the \"two brains\" under identical experimental conditions. We focus on evaluating the enzymatic activity profiles of key enzymes known to be sensitive to amyloid-beta exposure in the context of Alzheimer\u0026rsquo;s disease. By comparing these two populations, we aim to determine whether NSCs can serve as a surrogate model for enteric neurotoxicity and to evaluate the suitability of both models for future research on the toxicity of food-derived amyloidogenic proteins.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Materials and Reagents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnless otherwise specified, all chemical compounds and reagents were purchased from Sigma-Aldrich (St. Louis, MO, USA). Cell culture disposables were provided by Sarstedt (N\u0026uuml;mbrecht, Germany). Spectrophotometric measurements were performed using an Ultraspec 3100 Pro spectrophotometer (Amersham Biosciences, Warsaw, Poland).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Animals and Ethical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental procedures were conducted in accordance with the EU Directive 2010/63/EU and the International Council for Laboratory Animal Science (ICLAS) guidelines (Permission No. 50/2019). A total of 5 to 8 independent biological replicates (N=5\u0026minus;8) were used for each experimental group. Each biological replicate (N=1) represented a pooled batch of embryos obtained from a single pregnant female to ensure sufficient material and biological consistency. Mice were maintained under a 12-h light/dark cycle in an enriched environment with \u003cem\u003ead libitum\u003c/em\u003e access to food and water [13] .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Primary Neuronal Cultures (PR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimary neurons were isolated from the cerebral cortices of C57/BL6 mouse embryos (E14) in Hank\u0026rsquo;s Balanced Salt Solution (HBSS) supplemented with 50 U/mL penicillin, 50 mg/mL streptomycin, and 8 mM HEPES (Thermo Fisher Scientific, Waltham, MA, USA). Following the removal of blood vessels, the tissue was mechanically dissociated. The cell suspension was quantified using a hemocytometer and plated at a density of 9\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells/mL in Neurobasal medium supplemented with B27, 2 mM L-glutamine, 100 U/mL penicillin, and 100\u0026nbsp;\u0026mu;g/mL streptomycin (Thermo Fisher Scientific). Culture surfaces were pre-coated with poly-L-ornithine (Sigma-Aldrich) and laminin (Thermo Fisher Scientific). After 24 h, the medium was replaced with fresh medium and cultured for additional 7 days at 37\u003csup\u003e∘\u003c/sup\u003eC and 5%CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e[14].\u003csub\u003e\u0026nbsp;\u003c/sub\u003eCell purity and lineage identity were maintained according to previously validated and published protocols [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Neural Stem Cell Expansion and Differentiation (ENSC, NSC, \u003cem\u003eApoe\u003c/em\u003e KO-NSC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnteric neuronal stem cells (ENSC) were isolated from the embryonic large intestine (E14.5), while cortical neural stem cells (NSC) were obtained from the cerebral cortices of wild-type C57/BL6 or transgenic ApoE knockout (B6.129P2\u0026minus;Apoetm1Unc/J) embryos (E14.5). Following dissection in HBSS (supplemented with HEPES and antibiotics), cells were expanded as free-floating neurospheres in serum-free DMEM/F12 with GlutaMAX (Gibco) containing B27 supplement, 10 ng/mL bFGF (Gibco), and 20 ng/mL EGF (Corning). During the expansion phase, neurospheres were maintained without routine quantification and passaged every 2\u0026ndash;3 days. For experimental assays (passages 3\u0026ndash;5), neurospheres were dissociated into a single-cell suspension, quantified, and seeded in a monolayer at a density of 3\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells/cm\u003csup\u003e2\u003c/sup\u003e on poly-L-ornithine/laminin-coated dishes. After 24 h, the medium was replaced with growth-factor-free medium to induce differentiation for 7 days at 37\u003csup\u003e∘\u003c/sup\u003eC and 5%CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e[14]. Purity and lineage characteristics of the stem cell-derived cultures were consistent with established institutional standards and previous reports [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Microscopic Imaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.1. Live-Cell Phase-Contrast Microscopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMorphology and viability of live cell cultures were monitored throughout the expansion and differentiation phases using an inverted light microscope (Axiovert 25, Zeiss, Oberkochen, Germany). Phase-contrast images were captured at 40x magnification to monitor cell development\u0026nbsp;[14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.2. Immunocytochemistry (ICC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were fixed with 4% paraformaldehyde (PFA) for 15 min at RT, permeabilized, and blocked in 0.1% Triton X-100/PBS with 5% normal goat serum (NGS). Cells were incubated with primary antibodies (mouse anti-CNPase, 1:400, \u0026nbsp;Sigma Aldrich; rabbit anti-\u0026beta;-III-tubulin, 1:500, Cell Signaling Technology; mouse anti-S100\u0026beta;, 1:500, Sigma Aldrich; rabbit anti-GFAP, 1:200, Abcam) overnight, followed by AlexaFluor-conjugated secondary antibodies (488/555 nm anti-rabbit, 488/555 nm anti-mouse anti-mouse; Thermo Fisher Scientific) for 30 min at 37\u003csup\u003e∘\u003c/sup\u003eC. Nuclei were counterstained with DAPI stain (Thermo Fisher). Fluorescence images were acquired using a VS200 ASW inverted microscope (Olympus) at 60x magnification (2 image per each biological replicate) [13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6. Enzymatic Activity Assays and Protein Quantification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal protein content was assayed via the Bradford method using human immunoglobulin as the standard. All enzymatic activities were determined spectrophotometrically at 37\u003csup\u003e∘\u003c/sup\u003eC (unless otherwise noted) and are reported as specific activity (Units per mg of protein, U/mg, or as defined in the Results section). Cells were lysed using 0.2% Triton X-100.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.1. Tricarboxylic Acid (TCA) Cycle and Related Enzymes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAconitase (Aco, EC 4.2.1.3) and Isocitrate Dehydrogenase (IDH, EC 1.1.1.42):\u003c/strong\u003e Activities were determined via NADPH/NADP conversion at\u0026nbsp;\u0026lambda;=340nm. The Aco reaction buffer contained 0.05 M Tris-HCl (pH 7.4), 2 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 0.1 mM NADP, and 1 U IDH-NADP, 10 \u0026micro;M \u003cem\u003ecis-\u003c/em\u003eaconitane. The IDH buffer contained 0.05 M Tris-HCl (pH 7.4), 0.6 mM MgCl\u003csub\u003e2\u003c/sub\u003e, and 0.5 mM NADP, 10 \u0026micro;M isocitrate [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCitrate Synthase (CS, EC 4.1.3.7):\u003c/strong\u003e Measured by DTNB reduction at\u0026nbsp;\u0026lambda;=412nm in 0.1 M Tris-HCl (pH 8.0) with 0.015 mM acetyl-CoA and 0.2 mM DTNB, 0.2 mM oxaloacetate [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLactate Dehydrogenase (LDH, EC 1.1.1.27):\u003c/strong\u003e Assayed by NADH/NAD conversion at\u0026nbsp;\u0026lambda;=340nm in 0.1 M Tris-HCl (pH 7.4) with 0.2 mM NADH and 0.1 M pyruvate [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePyruvate Dehydrogenase Complex (PDHC, EC 1.2.4.1):\u003c/strong\u003e Determined using a cycling method. Citrate production was carried out for 30 min at 37\u003csup\u003e∘\u003c/sup\u003eC (0.1 M Tris-HCl pH = 8.3, 2 mM MgCl2, 10 mM DTT, 10 mM pyruvate, 2 mM thiamine pyrophosphate, 0.2 mM CoA, 2.5 mM oxaloacetate, 2 mM NAD, 0.15 U citrate synthase (EC 4.1.3.7).), followed by thermal termination (100\u003csup\u003e∘\u003c/sup\u003eC). Citrate levels were then measured spectrophotometrically at\u0026nbsp;\u0026lambda;=340nm using 0.2 U malate dehydrogenase and 0.1 U citrate lyase (0.1 M Tris-HCl pH = 7.4, 0.1 mM NADH) [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.2. Lipid Metabolism and Pentose Phosphate Pathway Enzymes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlucose-6-Phosphate Dehydrogenase (G6PD, EC 1.1.1.49):\u003c/strong\u003e Assayed via NADPH/NADP conversion at\u0026nbsp;\u0026lambda;=340nm in 0.05 M Tris-HCl (pH 7.4), 0.2 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 25 mM NADP and 10 mM glucose-6-phosphate [15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFatty Acid Synthase (FAS, EC 2.3.1.85):\u003c/strong\u003e Measured at\u0026nbsp;\u0026lambda;=340nm in 0.05 M Tris-HCl (pH 7.4) with 0.1 M DTT, 0.1 M EDTA, 0.01 mM NADPH and 6 mg/ml malonyl-CoA [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarnitine Acetyltransferase (CAT, EC 2.3.1.7):\u003c/strong\u003e Determined via a cycling DTNB/TNB reduction technique at\u0026nbsp;\u0026lambda;=412nm following acetyl-carnitine production (0.1 M Tris-HCl pH = 7.4, 0.15 M carnitine, 10 mg/ml acetyl-CoA) reaction termination with 40% trichloroacetic acid and CoA level assessment (0.05 M Tris-HCl pH = 7.4, 0.05 M EDTA, 2 mM DTNB) [17].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcyl-CoA Oxidase 1 (ACOX1, EC 1.3.3.6):\u003c/strong\u003e Activity was measured at\u0026nbsp;\u0026lambda;=500nm and 30∘C using the phenol/quinoneimine conversion technique in 45 mM MES (pH 8.0) with 4-aminoantipyrine, FAD, phenol, peroxidase and 5 mg/ml palmityl-CoA [18].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcetyl-CoA Synthetase (ACS, EC 6.2.1.1) and ATP-Citrate Lyase (ACLY, EC 2.3.3.8):\u003c/strong\u003e Measured by NAD(P)H conversion at\u0026nbsp;\u0026lambda;=340nm in 0.05 M Tris-HCl (pH = 7.4), 10 mM Acetate, 4 mM ATP, 1 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 10 mM DTT and 6 mg/ml CoA [19, 20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData distribution was confirmed as normal using the Kolmogorov\u0026ndash;Smirnov test. Comparative analysis was performed using Student\u0026rsquo;s T-test (for two groups) or One-way ANOVA followed by Tukey\u0026rsquo;s multiple comparison post-hoc test. Multivariate data analysis, including Principal Component Analysis (PCA) and Permutational Multivariate Analysis of Variance (PERMANOVA), was performed to assess global metabolic differences between lineages using the vegan and factoextra packages. Data are presented as mean \u0026plusmn; SD. All statistical procedures were conducted in RStudio (R version 4.5.2).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Lineage Commitment and Model Validation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhase-contrast microscopy of live cultures at 7 DIV revealed no distinct morphological differences between differentiated ENSC, NSC, and \u003cem\u003eApoe\u003c/em\u003e KO lineages. All stem cell-derived groups formed comparable monolayers characterized by a similar cellular architecture and growth pattern. In contrast, primary cortical neurons (PR) exhibited a markedly lower overall cell density compared to the stem cell-derived lineages. Among the three stem cell-derived models, no significant variations in cell density or general morphology were observed under the identical experimental conditions used for differentiation (Fig. 1A).\u003c/p\u003e\n\u003cp\u003eTo further validate the cellular composition, we performed immunocytochemical staining for neuronal and glial markers (Fig. 1A-B). Quantitative analysis (Fig. 1B) confirmed that the differentiation protocol yielded similar lineage distributions across the ENSC, NSC, and \u003cem\u003eApoe\u003c/em\u003e KO groups. GFAP\u003csup\u003e+\u003c/sup\u003e glial cells constituted the predominant population, accounting for approximately 60\u0026ndash;80% of total cells, while \u0026beta;III-tubulin\u003csup\u003e+\u003c/sup\u003e neurons represented approximately 20\u0026ndash;25% of the culture. A small, stable subpopulation of CNPase\u003csup\u003e+\u003c/sup\u003e cells (typically below 10%) was consistently identified in all groups (Fig. 1B). The high degree of morphological and compositional similarity between the stem cell-derived lineages ensures that the metabolic divergences identified in the subsequent enzymatic profiling reflect intrinsic biochemical programming rather than variations in cell type proportions or culture density.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Functional Enzymatic Profiling of Enteric and Cortical Lineages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo establish the bioenergetic baseline for the proposed cell models, we quantified the activities of key enzymes involved in glycolysis, the tricarboxylic acid (TCA) cycle, and lipid metabolism (Table 1). Our results revealed fundamental differences in the metabolic \u0026quot;engines\u0026quot; of enteric versus cortical lineages, despite their shared embryonic origin (E14.5).\u003c/p\u003e\n\u003cp\u003eThe enteric neural stem cells (ENSCs) exhibited a significantly more robust metabolic profile compared to both cortical neural stem cells (NSCs) and primary cortical neurons (PR) (Table 1). Notably, the activity of lactate dehydrogenase (LDH), a key indicator of glycolytic flux and anaerobic capacity, was nearly 1.6-fold higher in ENSCs (1175.0\u0026plusmn;281.4 nmol/min/mg) than in NSCs (722.6\u0026plusmn;206.7 nmol/min/mg; p\u0026lt;0.05). Furthermore, citrate synthase (CS) activity, a widely accepted biomarker for mitochondrial content and TCA cycle entry, was over 3.5-fold higher in ENSCs compared to NSCs (212.9\u0026plusmn;85.8 vs. 58.9\u0026plusmn;21.5 nmol/min/mg), suggesting a substantially higher oxidative capacity in the enteric niche (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Divergent Pathways in Lipid Metabolism and \u003cem\u003eDe Novo\u003c/em\u003e Lipogenesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA defining characteristic of the ENSC profile was the significantly elevated activity of enzymes supporting cytoplasmic lipid synthesis. ATP-citrate lyase (ACLY), which facilitates the transport of mitochondrial-derived citrate into the cytoplasm and its conversion back to acetyl-CoA for \u003cem\u003ede novo\u003c/em\u003e lipogenesis, was significantly more active in ENSCs than in cortical cells (89.3\u0026plusmn;12.2 vs. 53.2\u0026plusmn;26.4 nmol/min/mg; p\u0026lt;0.05) (Table 1).\u003c/p\u003e\n\u003cp\u003eThis \u0026quot;pro-lipid\u0026quot; signature was further corroborated by the activity of isocitrate dehydrogenase (IDH). Given the use of NADP\u003csup\u003e+\u003c/sup\u003e as a cofactor in our assays, the measured activity primarily reflects the cytoplasmic/NADP-dependent isoforms, which play a critical role in supplying both citrate-derived intermediates and NADPH required for reductive biosynthesis. While primary cortical neurons (PR) displayed high IDH activity (81.7\u0026plusmn;24.9 nmol/min/mg), the basal enteric profile remained consistently robust compared to cortical progenitors. Additionally, carnitine acetyltransferase (CAT) activity was over four times higher in ENSCs than in NSCs (13.8\u0026plusmn;4.6 vs. 3.1\u0026plusmn;0.6 nmol/min/mg; p\u0026lt;0.05), confirming that ENSCs are uniquely optimized for intensive lipid-related metabolic turnover (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Global Bioenergetic Mapping and Multivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the global bioenergetic relationships between the lineages, we performed a Principal Component Analysis (PCA) based on the activities of core energy-metabolism enzymes: LDH, PDHC, Aconitase, and IDH (Fig. 2A).\u003c/p\u003e\n\u003cp\u003eThe PCA model accounted for 75.4% of the total variance (PC1: 45.3%; PC2: 30.1%). The multivariate analysis revealed a striking metabolic overlap between primary cortical neurons (PR) and cortical neural stem cells (NSCs), with their 95% confidence ellipses largely coinciding. This similarity indicates a consistent metabolic program shared by cortical progenitors and their differentiated counterparts, characterized by a specific balance between pyruvate oxidation (PDHC) and TCA cycle flux (Fig. 2A).\u003c/p\u003e\n\u003cp\u003eIn contrast, ENSCs formed a distinct and separate cluster, clearly segregated from the cortical models along the PC2 axis. This global divergence was further validated using PERMANOVA, which confirmed that cell lineage is a significant determinant of the enzymatic profile (F=6.435,R2=0.518,p=0.0079). Pairwise PERMANOVA comparisons underscored that ENSCs differ significantly from both NSCs (p=0.0229) and PR (p=0.0237). Crucially, no significant metabolic difference was found between the two cortical models, NSC and PR (p=0.3537), suggesting that standard cortical surrogates fail to recapitulate the unique bioenergetic fingerprint required for accurate enteric modeling (Fig. 2A).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Comparative Metabolic Characterization of ENSC, NSC, and\u0026nbsp;\u003c/strong\u003e\u003cem\u003eApoe\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;KO Lineages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine whether the unique metabolic signature of the enteric niche could be recapitulated in cortical lineages by disrupting lipid transport, we compared ENSCs with wild-type NSCs (NSC) and \u003cem\u003eApoe\u003c/em\u003e knockout NSCs (\u003cem\u003eApoe\u003c/em\u003e KO NSC). This comparison aimed to test if ApoE deficiency forces cortical cells toward a \u0026quot;pro-lipid\u0026quot; enteric phenotype. However, our results demonstrate that the loss of ApoE leads to a profound metabolic reorganization that does not mimic the enteric profile, but instead induces a distinct, potentially pathological bioenergetic state (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1. Suppression of Glycolytic and Early TCA Cycle Flux in \u003cem\u003eApoE\u003c/em\u003e KO Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA defining feature of the \u003cem\u003eApoe\u003c/em\u003e KO model was the drastic collapse of standard bioenergetic throughput. LDH activity in \u003cem\u003eApoe\u003c/em\u003e KO cells was over 10-fold lower than in NSCs (73.1\u0026plusmn;50.6 vs. 722.6\u0026plusmn;206.7 nmol/min/mg; p\u0026lt;0.001) and nearly 16-fold lower than in ENSCs (1175.0\u0026plusmn;281.4 nmol/min/mg). Furthermore, activities of enzymes initiating the TCA cycle and associated pathways were significantly suppressed; aconitase activity dropped to 10.6\u0026plusmn;2.2 nmol/min/mg, and IDH activity reached its lowest levels in this group (8.3\u0026plusmn;4.5 nmol/min/mg; p\u0026lt;0.05). Since the IDH assay was based on NADP\u003csup\u003e+\u003c/sup\u003e/NADPH turnover, these results specifically indicate a deficit in the cell\u0026rsquo;s capacity to generate NADPH via this pathway, which is critical for both reductive biosynthesis and antioxidant defense.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2. Shift Toward Alternative Carbon Utilization and Peroxisomal Oxidation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn contrast to the physiologically balanced lipogenic profile of ENSCs, \u003cem\u003eApoe\u0026nbsp;\u003c/em\u003eKO NSC cells exhibited a massive induction of acetyl-CoA synthetase (ACS) (210.5\u0026plusmn;149.4 vs. 17.7\u0026plusmn;4.8 nmol/min/mg in NSC; p\u0026lt;0.05), suggesting a forced reliance on acetate as a metabolic substrate. This was accompanied by a significant increase in acyl-CoA oxidase 1 (ACOX1) activity (48.6\u0026plusmn;10.6 nmol/min/mg; p\u0026lt;0.05), a hallmark of peroxisomal \u0026beta;-oxidation. Although glucose-6-phosphate dehydrogenase (G6PD) activity was elevated in \u003cem\u003eApoe\u003c/em\u003e KO NSC cells (88.8\u0026plusmn;33.4 nmol/min/mg), potentially as a compensatory mechanism to supply NADPH, the activity of fatty acid synthase (FAS) remained unchanged (45.2\u0026plusmn;30.0 nmol/min/mg). This indicates that the metabolic reprogramming in the absence of ApoE is driven by stress adaptation rather than the specialized lipogenic specialization seen in ENSCs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6. Multivariate Bioenergetic Mapping (PCA Panel B)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe global divergence of these models was further confirmed by multivariate mapping (Fig. 2B). The PCA model, encompassing 9 enzymatic variables, explained 68.1% of the total variance. \u003cem\u003eApoe\u0026nbsp;\u003c/em\u003eKO NSC cells (purple ellipse) formed a highly distinct cluster, separated from both NSCs and ENSCs primarily along the PC1 axis (45.5% of variance). This separation was strongly driven by the loading vectors of ACS, ACOX1, and G6PD, confirming the shift toward an acetate-peroxisomal axis. Global PERMANOVA analysis validated that the metabolic fingerprint was significantly defined by the cell lineage and genotype (p=0.0079), with pairwise comparisons confirming that the \u003cem\u003eApoe\u003c/em\u003e KO profile remained significantly distinct from the ENSC signature (p\u0026lt;0.05). These data underscore that ApoE deficiency fails to recapitulate the enteric metabolic imprinting.\u003c/p\u003e\n\u003cp\u003eTable 1. Comparison of enzymatic profiles across primary cell lines\u003c/p\u003e\n\u003ctable style=\"width: 4.6e+2pt;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell line:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eENSC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eApoe\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;KO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eEnzyme activity \u003cem\u003enmol/min/mg protein\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1175.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e281.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e722.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e206.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e73.1***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e50.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e819.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e98.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePDHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e21.0*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAconitase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e31.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e39.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eIDHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e44.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8.3*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e81.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e212.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e85.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e58.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e264.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e252.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eACOX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e48.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e210.5*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e149.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eG6PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e88.8*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eACLY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e89.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e53.2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e83.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e42.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; SD from N=5\u0026ndash;8 independent biological replicates (each replicate represents a pooled batch of embryos from a single pregnant female). Statistical significance was determined by one-way ANOVA followed by Tukey\u0026rsquo;s multiple comparisons post-hoc test. Significance levels are indicated as follows: *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001 versus the ENSC group.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ENSC enteric neural stem cells, NSC cortical neural stem cells, Apoe KO apolipoprotein E knockout cortical neural stem cells, PR primary cortical neurons, LDH lactate dehydrogenase, PDHC pyruvate dehydrogenase complex, IDHC isocitrate dehydrogenase, SC citrate synthase, ACOX1 acyl-CoA oxidase 1, ACS acetyl-CoA synthetase, G6PD glucose-6-phosphate dehydrogenase, ACLY ATP-citrate lyase, FAS fatty acid synthase, CAT carnitine acetyltransferase.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1. The Dual Nature of Protein Fibrillation: Industrial Utility vs. Pathogenic Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe phenomenon of protein fibrillation, once primarily the domain of clinical pathology, has emerged as a significant factor in modern food science and technology. Thermal processing, such as the boiling of hen eggs, induces the fibrillation of key proteins like ovalbumin (OVA) and lysozyme (LYZ) [21, 22]. Traditionally, this structural transformation has been viewed through a positive lens by gastroenterologists and nutritionists, as it can attenuate the allergenic potential of eggs and enhance postprandial satiety. From a textural engineering perspective, these fibrils have revolutionized the development of vegetarian meat substitutes, where they serve as scaffolds that faithfully replicate the fibrous architecture of animal muscle tissue\u0026nbsp;[6, 23].\u003c/p\u003e\n\u003cp\u003eHowever, the structural characteristics that make Food Protein Amyloid Fibrils (FPAFs) industrially attractive\u0026mdash;their highly ordered \u0026beta;-sheet architectures, remarkable mechanical rigidity, and resistance to environmental degradation\u0026mdash;are the same traits that define pathogenic amyloid proteins [23]. The pathomechanism of amyloidosis, particularly Alzheimer\u0026rsquo;s disease (AD), is fundamentally rooted in the neurotoxicity of soluble amyloid-beta (1-42) polymers, which share a cross-\u0026beta; structural motif with FPAFs [23]. This structural homology raises critical questions regarding the safety of a global food chain increasingly enriched with amyloid-like structures . Due to the extensive hydrogen bonding and the \u0026quot;dry\u0026quot; packing interface between their side chains, these fibrils are arguably the most stable structures known to be adopted by polypeptides\u0026nbsp;[23, 24]. They possess melting temperatures and detergent resistance far exceeding those of globular proteins. While food processing often involves extreme conditions (e.g., low pH or prolonged heating), the question remains whether these fibrils, once formed, can withstand the varied chemical and physical rigors of the human digestive tract, especially in processing steps involving intense shear forces, such as the formation of foams and emulsions\u0026nbsp;[23]\u0026nbsp;[7].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2. Amyloid Biohazards in the Human Food Chain\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch has identified AA amyloid (amyloid A) deposits in the tissues of livestock deemed healthy for slaughter. Studies indicate a prevalence of 1% to 15% in cattle, with the variance largely driven by animal age\u0026mdash;Italian cattle, being significantly older than their Swedish counterparts, showed the highest incidence\u0026nbsp;[25]. Similar findings have been reported in sheep and directly in gourmet products such as foie gras\u0026nbsp;[26, 27].\u003c/p\u003e\n\u003cp\u003eUnder laboratory conditions, common food proteins like lysozyme and \u0026beta;-lactoglobulin have been shown to form amyloid-like aggregates. While some argue that these food-derived fibrils do not directly accelerate A\u0026beta; aggregation in the brain, the broader spectrum of systemic amyloidosis cannot be ignored [28]. Amyloidosis is a heterogeneous group of diseases involving the deposition of toxic, insoluble protein aggregates in multiple organs, leading to restrictive cardiomyopathy, renal failure, and severe soft-tissue involvement\u0026nbsp;[29]. Cardiac involvement is particularly lethal, with a 5-year survival rate of less than 10% in advanced cases. Renal dysfunction, characterized by asymptomatic albuminuria progressing to nephrotic syndrome, is a hallmark of hereditary and systemic forms involving lysozyme (ALys) and AA amyloid\u0026nbsp;[30, 31].\u003c/p\u003e\n\u003cp\u003eThe most pervasive of these conditions remains Alzheimer\u0026rsquo;s disease. AD is characterized by the accumulation of soluble A\u0026beta;(1-42) oligomers in the brain, leading to the degradation of cholinergic neurons, synaptic dysfunction, and chronic neuroinflammation\u0026nbsp;[10, 32]. Emerging evidence suggests that processed, protein-rich foods could serve as a source of protease-resistant amyloid aggregates that compromise nutritional value and potentially exaggerate the etiology of amyloid-related diseases [33, 34].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3. Evaluating the Sensitivity of Current Experimental Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite statistical evidence linking neurodegenerative sensitivity to amyloid-like proteins, the current literature predominantly relies on the Caco-2 (colorectal adenocarcinoma) or HepG2 (immortalized liver) cell lines to assess the toxicity of food fibrils [7, 35\u0026ndash;38]. Both Caco-2 and HepG2 are secondary cancer-derived lines, which are inherently more resistant to cytotoxic insults than neural tissue [39]. Studies have shown that while cancer lines might survive certain exposures, primary neurons are significantly more sensitive to apoptosis and necrosis\u003cstrong\u003e\u003cem\u003e.\u003c/em\u003e\u003c/strong\u003e For example, the toxic response of neurons to quercetin is far more pronounced than that of neuroblastoma cell lines\u0026nbsp;[40].\u003c/p\u003e\n\u003cp\u003eFurthermore, the mechanism of A\u0026beta; neurotoxicity is closely tied to neuron-specific proteins. The presence of Tau protein and cyclin-dependent kinase 5 (CDK5) in neurons is a critical trigger for polymer-induced neurotoxicity\u0026mdash;proteins that are absent or significantly different in colorectal epithelial cells [41\u0026ndash;43]. Recent evidence demonstrates that while Caco-2 cells may remain unchanged by animal-derived amyloid fibrils, these same fibrils cause significant upregulation of neurodegenerative disease\u0026nbsp;proteins in neural models\u0026nbsp;[7]. Experiments in \u003cem\u003eC. elegans\u003c/em\u003e have further confirmed that certain amyloid species can cross the intestinal membrane and diffuse into surrounding tissues [44].\u003c/p\u003e\n\u003cp\u003eThis disparity necessitates the use of more sensitive models, particularly those reflecting the enteric nervous system (ENS). The ENS is an essential link in gastrointestinal pathologies like necrotizing enterocolitis, diabetic gastroparesis, and Hirschsprung disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4. The Impact of Digestion and Pathophysiological Conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA prevailing defense for the safety of food fibrils is their presumed degradation by gastrointestinal proteases like pepsin, trypsin, and chymotrypsin. Indeed, under optimal conditions, ThT staining and atomic force microscopy indicate that these enzymes can reduce fibril levels below detection limits\u0026nbsp;[7, 45, 46]. However, recent reports\u0026nbsp;suggesting complete degradation to non-toxic peptides are often based on models that simulate ideal physiological conditions. These models fail to account for the rising prevalence of gastrointestinal dysfunctions [7].\u003c/p\u003e\n\u003cp\u003eConditions such as hypochlorhydria (frequently induced by proton pump inhibitors), pernicious anemia, or chloride imbalances directly impair pepsin activity and alter gastric pH. In such pathophysiological states, the digestion of food fibrils may be arrested at the stage of soluble oligomers\u0026nbsp;and protofibrils [47]. These intermediate forms are far more dangerous than mature, insoluble fibrils; they are mobile, highly toxic, and capable of mucosal penetration. \u003cem\u003eIn vitro\u003c/em\u003e evidence even suggests that partial pepsin digestion can increase the toxicity of certain materials for neurons by releasing these dangerous soluble species [10].\u003c/p\u003e\n\u003cp\u003eOur findings indicate that ENSCs (enteric neural stem cells) possess a unique enzymatic profile, including 4-fold higher catalase (CAT) activity and high ACLY and SC activities, compared to cortical NSCs (Table 1). This may represent an evolutionary adaptation allowing these cells to neutralize proteotoxic stress that would be fatal to CNS neurons. The lack of neural-based models in existing food fibril literature makes our findings vital for understanding the true risks within the gut-brain axis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5. Establishing the Primary ENSC and NSC Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe technical difficulty of isolating primary ENSCs is a major hurdle in neurobiological research. Cellular yields from adult and newborn mice are typically low, and their proliferative capacity \u003cem\u003ein vitro\u003c/em\u003e is often poor\u0026nbsp;[48]. While some teams have found that reducing oxygen to 5% improves isolation efficiency in adult swine models\u0026nbsp;[49], we focused on E14.5 mouse embryos to maximize isolation efficiency and biomass (Fig. 1 A-B).\u003c/p\u003e\n\u003cp\u003eOur protocol yielded cultures with a complex, network-like morphology containing \u0026beta;III-tubulin (+) neurons, GFAP (+) / S100B (+) glial cells, and CNPase (+) myelinating cells (Fig. 1 A-B). This composition allows for the study of the neuro-glial unit, which is\u0026nbsp;critical since the neurotoxicity of amyloid proteins often involves the disruption of energy production pathways, including the tricarboxylic acid (TCA) cycle [50, 51].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6. Model Validation: NSC as a Tool for CNS Neuro-Glial Interplay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address these limitations, we compared primary cortical neurons (PR) with cortical neural stem cells (NSC). Our findings revealed a striking overlap in the metabolic profiles of these two groups, as visualized in the PCA analysis where their 95% confidence ellipses largely coincided (Fig. 2A). Importantly, this does not merely suggest that NSCs are a substitute for neurons. Instead, it validates the differentiated NSC model as a robust and essential tool for evaluating neuro-glial interactions within the CNS [52].\u003c/p\u003e\n\u003cp\u003eUnlike isolated primary neurons, which often lack a supporting glial environment, our differentiated NSC cultures yielded a balanced population of \u0026beta;III-tubulin+ neurons (20-25%) and GFAP+/S100\u0026beta;(+) glial cells (60-80%)(Fig. 1 A-B)[14]. This composition is crucial for modeling the metabolic and inflammatory responses of the brain, as glial cells are active participants in both neuroprotection and the progression of amyloid-induced damage [13]. The metabolic consistency between PR and differentiated NSCs confirms that the latter can faithfully replicate the enzymatic and bioenergetic environment of the cortical niche while providing the biomass necessary for detailed molecular studies (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7. The Enteric \u0026quot;Second Brain\u0026quot;: ENSC vs. NSC Metabolic Divergence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe most\u0026nbsp;significant finding of this study is the definitive metabolic segregation of enteric neural stem cells (ENSCs) from their cortical counterparts. Despite sharing the same embryonic origin (E14.5), ENSCs form a completely distinct cluster in multivariate mapping. This global divergence (confirmed by PERMANOVA, p=0.0079) disqualifies standard cortical models from being used to accurately predict toxicity within the enteric nervous system (ENS)(Fig. 2 A).\u003c/p\u003e\n\u003cp\u003eENSCs exhibited a significantly more \u0026quot;aggressive\u0026quot; metabolic profile. The nearly 1.6-fold higher activity of Lactate Dehydrogenase (LDH) (1175.0 vs 722.6 nmol/min/mg) in ENSCs points to a strong preference for aerobic glycolysis (the Warburg effect) (Table 1). This is a hallmark of highly regenerative stem cells situated in niches\u0026mdash;like the intestinal wall\u0026mdash;where oxygen levels and chemical environments fluctuate constantly. [53] Simultaneously, ENSCs maintained a massive mitochondrial reserve, with citrate synthase (SC) activity being over 3.5 times higher than in NSCs (212.9 vs 58.9) (Table 1). The ENS must function autonomously in a volatile environment (fluctuating pH, microbial metabolites); this high mitochondrial capacity likely provides the energy necessary for such high-intensity metabolic turnover [54].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7.1. The Anabolic Synergy of the \u0026quot;Second Brain\u0026quot;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe defining characteristic of the ENSC is its \u0026quot;aggressive\u0026quot; anabolic signature, driven by the synergy of citrate synthase (SC) produces citrate in the mitochondria (212.9 in ENSC vs 58.9 in NSC), ATP-citrate lyase (ACLY) cleaves cytosolic citrate back into\u0026nbsp;acetyl-CoA (89.3 vs 53.2); and carnitine ccetyltransferase (CAT) buffers the acetyl-CoA pool through the carnitine system (13.8 vs 3.1)(Table 1). This system is uniquely optimized for \u003cem\u003ede novo\u003c/em\u003e lipogenesis and rapid membrane reconstruction [55]. While cortical NSCs direct glucose primarily toward the pentose phosphate pathway (higher G6PD: 54.7 vs 28.0) for ribose and NADPH production, ENSCs \u0026quot;pump\u0026quot; their carbon directly into cell mass (lipids) and rapid energy (LDH) (Table 1). The 4-fold increase in CAT suggests a superior ability to move two-carbon units where needed, providing metabolic flexibility that cortical neurons lack. This flexibility may also regulate histone acetylation, potentially explaining the greater phenotypic plasticity of enteric cells [56].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7.2. Alzheimer\u0026rsquo;s as \u0026quot;Type 3 Diabetes\u0026quot; and the Braak Hypothesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn neurochemistry, AD is often referred to as \u0026quot;Type 3 Diabetes\u0026quot; due to systemic insulin resistance and metabolic dysfunction [57]. Our model highlights significant differences in how the \u0026quot;two brains\u0026quot; handle metabolic stress (Table 1, Fig. 2 A-B). The high glycolytic and mitochondrial turnover of the ENS might make it more sensitive to toxins that disrupt lipid metabolism\u0026mdash;precisely the effect seen with some food-derived amyloids (Table 1).\u003c/p\u003e\n\u003cp\u003eFurthermore, our results align with the Braak Hypothesis, which suggests that neurodegenerative pathologies (like Parkinson\u0026rsquo;s or AD) may originate in the enteric plexuses before \u0026quot;traveling\u0026quot; to the CNS via the vagus nerve [58]. The high metabolic rate of ENSCs could paradoxically make them more vulnerable; high mitochondrial turnover generates more reactive oxygen species (ROS), which can act as a primer for protein aggregation upon exposure to dietary fibrils [59]. Using the activities of aconitase (Aco) and isocitrate dehydrogenase (IDH) as \u0026quot;biosensors\u0026quot; allows us to detect this \u0026quot;cellular poisoning\u0026quot; before morphological degeneration occurs [60, 61]. Aconitase, with its Fe-S clusters, is particularly sensitive to the ROS generated by A\u0026beta; oligomers [62].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.8. The \u003cem\u003eApoe\u0026nbsp;\u003c/em\u003eKO Model: Metabolic Collapse vs. Enteric Adaptation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test if disrupting lipid transport could \u0026quot;force\u0026quot; a cortical cell into an enteric-like state, we compared ENSCs with \u003cem\u003eApoe\u0026nbsp;\u003c/em\u003eknockout (\u003cem\u003eApoe\u003c/em\u003e KO) NSCs. ApoE is a critical risk factor for AD and is expressed in the gut as well as the brain [63].\u003c/p\u003e\n\u003cp\u003eContrary to our initial hypothesis, ApoE deficiency did not mimic the enteric profile. Instead, it triggered a bioenergetic collapse, highlighted by a 10-fold drop in LDH activity (73.1 vs 722.6 in NSC) (Table 1). \u003cem\u003eApoe\u0026nbsp;\u003c/em\u003eKO cells entered a \u0026quot;rescue metabolism\u0026quot; state, marked by a massive induction of Acetyl-CoA Synthetase (ACS) (210.5 vs 17.7) and acyl-CoA oxidase 1 (ACOX1) (48.6 vs 24.6) (Table 1). ACS allows cells to utilize acetate as a substrate [64]. Since acetate is a major short-chain fatty acid (SCFA) produced by the gut microbiome, our data suggest that in the absence of ApoE, neural cells become dangerously dependent on microbial metabolites.\u003c/p\u003e\n\u003cp\u003eThe induction of ACOX1 indicates increased peroxisomal fatty acid oxidation, which generates high levels of oxidative stress [65, 66]. This suggests that individuals with risk genotypes (like ApoE4) possess a lower metabolic threshold in their enteric nervous system [67]. Their \u0026quot;second brain\u0026quot; is already running on a \u0026quot;rescue\u0026quot; program, leaving no reserve to fight the proteotoxic stress of food-derived amyloid fibrils. This could explain why certain populations are more susceptible to the gut-initiated spread of neurodegeneration.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe present study demonstrates that ENSCs possess a unique and robust metabolic identity that is fundamentally distinct from cortical NSCs. This identity is defined by a high-capacity mitochondrial-lipogenic axis (SC-ACLY-CAT) and a glycolytic preference (LDH) that likely protects them in the volatile gastrointestinal environment but also increases their vulnerability to dietary metabolic toxins. Our findings disqualify cortical surrogates and secondary lines like Caco-2 for accurate enteric modeling. By establishing enzymatic activities as early \u0026quot;biosensors,\u0026quot; we provide a new framework for evaluating the long-term neurotoxic potential of food protein amyloid fibrils and their contribution to the global burden of neurodegenerative disease.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.W. (investigation), O.P. (formal analysis), A.G. (methodology, writing\u0026mdash;review and editing), J.B. (methodology, writing\u0026mdash;review and editing), M.S.B. (investigation, methodology, writing\u0026mdash;review and editing), M.Z. (conceptualization, methodology, resources, data curation, writing\u0026mdash;original draft preparation, writing\u0026mdash;review and editing, visualization, supervision, project administration, funding acquisition). All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Medical University of Gdańsk through the \u0026quot;Excellence Initiative \u0026ndash; Research University\u0026quot; (IDUB) framework, specifically within the \u0026quot;Young Creator of Science\u0026quot; grant program (grant no. 71-01409/ K01 0003279 and 71-01419/ K07 0004722).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available from the corresponding author on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGrundy, Schemann, Wood (2000) A tale of two brains, one little and one big. 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Oxid Med Cell Longev 2021:7726058. https://doi.org/10.1155/2021/7726058 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"neurochemical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nere","sideBox":"Learn more about [Neurochemical Research](https://www.springer.com/journal/11064)","snPcode":"11064","submissionUrl":"https://submission.nature.com/new-submission/11064/3","title":"Neurochemical Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"enteric neural stem cells, neural stem cells, Apoe-knockout, primary neurons, enzymatic profile","lastPublishedDoi":"10.21203/rs.3.rs-9517347/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9517347/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe enteric nervous system (ENS) is increasingly implicated in the gut-brain axis pathophysiology of neurodegenerative diseases. Given the structural similarities between pathogenic amyloid-beta and industrial food protein amyloid fibrils (FPAFs), assessing enteric neurotoxicity is critical. However, current immortalized cell models fail to recapitulate specific neuro-glial vulnerabilities.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe established a robust primary model by isolating enteric neural stem cells (ENSCs) and cortical neural stem cells (NSCs) from E14.5 mouse embryos. Following comparable neuro-glial differentiation, we performed functional enzymatic profiling of glycolysis, the TCA cycle, and lipid metabolism. These profiles were mapped via multivariate analysis and compared alongside an \u003cem\u003eApoe\u003c/em\u003e knockout (KO) NSC lineage.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eENSCs demonstrated a fundamentally distinct and more robust metabolic profile than cortical NSCs. ENSCs exhibited a potent mitochondrial-lipogenic axis\u0026mdash;marked by significantly elevated citrate synthase, ATP-citrate lyase, and carnitine acetyltransferase\u0026mdash;alongside a higher glycolytic flux (elevated lactate dehydrogenase). Principal Component Analysis confirmed ENSCs form a completely separate bioenergetic cluster. Furthermore, \u003cem\u003eApoe\u003c/em\u003e deficiency in cortical cells did not replicate this enteric phenotype but instead triggered a bioenergetic collapse and a stress-driven shift toward acetate utilization and peroxisomal oxidation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe unique metabolic identity of the ENS disqualifies cortical or immortalized surrogates for accurate enteric modeling. This primary ENSC model, utilizing enzymatic activities as early biosensors, provides a crucial framework for evaluating the neurotoxic risks of dietary amyloidogenic proteins.\u003c/p\u003e","manuscriptTitle":"Comparative Enzymatic Profiling of Enteric vs. Cortical Neural Stem Cells: Establishing a Methodological Foundation for Modeling Enteric Neurodegeneration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 20:09:11","doi":"10.21203/rs.3.rs-9517347/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"286993602231698663944792452315047193473","date":"2026-05-03T14:56:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234014897721156333117062968905280520280","date":"2026-04-30T07:07:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11536938195387698839634963465257782413","date":"2026-04-29T20:55:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29940746476078751873578244497358629736","date":"2026-04-29T16:55:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113306683570119871075042967531432592532","date":"2026-04-29T15:42:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164423124496235029384845631791683120173","date":"2026-04-28T01:12:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T14:15:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T14:08:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-25T11:15:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Neurochemical Research","date":"2026-04-24T12:25:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"neurochemical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nere","sideBox":"Learn more about [Neurochemical Research](https://www.springer.com/journal/11064)","snPcode":"11064","submissionUrl":"https://submission.nature.com/new-submission/11064/3","title":"Neurochemical Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b283327c-fe28-40b9-82f1-ebfbff1fa8f5","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"286993602231698663944792452315047193473","date":"2026-05-03T14:56:35+00:00","index":37,"fulltext":""},{"type":"reviewerAgreed","content":"234014897721156333117062968905280520280","date":"2026-04-30T07:07:58+00:00","index":36,"fulltext":""},{"type":"reviewerAgreed","content":"11536938195387698839634963465257782413","date":"2026-04-29T20:55:32+00:00","index":35,"fulltext":""},{"type":"reviewerAgreed","content":"29940746476078751873578244497358629736","date":"2026-04-29T16:55:43+00:00","index":34,"fulltext":""},{"type":"reviewerAgreed","content":"113306683570119871075042967531432592532","date":"2026-04-29T15:42:48+00:00","index":33,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T20:09:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 20:09:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9517347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9517347","identity":"rs-9517347","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}